Comparison of statistical prediction methods for estimating field-scale clay content using different combinations of ancillary variables

The need for spatial information on soil properties at the field level is i ncreasing, particularly for its applications in precision agriculture and e nvironmental management. One important soil property is clay content; howev er, costs involved with obtaining soil data at the field scale are prohibit ive. Geostatistical techniques have been used with some success to improve the accuracy of spatial prediction of soil properties, especially those bas ed on easy-to-obtain ancillary information. There is also, however, the nee d to determine optimal spacing for generating the ancillary data for spatia l prediction. In this paper, we used ancillary variables along with spatial prediction models to determine an optimal method for estimating clay conte nt at the field scale. We also determined the optimal spacing for generatin g the ancillary data for spatial prediction. The ancillary variables used w ere apparent soil electrical conductivity (ECa) obtained with EM38 and EM31 and digitized hands (red, green, and blue) of aerial photographs of the ba re soil. The spatial pre diction models tested are generalized additive mod els using various combinations of ancillary data (e.g., ECa and red, green, and blue data) and the geostatistical methods of ordinary-, regression- an d co-kriging. The results suggest that the linear regression of average cla y content with ECa (EM38) data used in combination with kriging of regressi on residuals was most accurate (RMSE = 3.03). The generation of ECa data on 24-m transect spacing was optimal for prediction. Doubling and tripling th e transect spacing (i.e., 48 and 72 m) cause relative reductions in precisi on of 17% and 12%, respectively.